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2016, Volume 17, Issue 9

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Frontiers of Information Technology & Electronic Engineering >> 2016, Volume 17, Issue 9 doi: 10.1631/FITEE.1500447

Attribute reduction in interval-valued information systems based on information entropies

. School of Computer Science and Technology, Tianjin University, Tianjin 300350, China.. College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China

Available online: 2016-10-08
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Abstract

Interval-valued data appear as a way to represent the uncertainty affecting the observed values. Dealing with interval-valued information systems is helpful to generalize the applications of rough set theory. Attribute reduction is a key issue in analysis of interval-valued data. Existing attribute reduction methods for single-valued data are unsuitable for interval-valued data. So far, there have been few studies on attribute reduction methods for interval-valued data. In this paper, we propose a framework for attribute reduction in interval-valued data from the viewpoint of information theory. Some information theory concepts, including entropy, conditional entropy, and joint entropy, are given in interval-valued information systems. Based on these concepts, we provide an information theory view for attribute reduction in interval-valued information systems. Consequently, attribute reduction algorithms are proposed. Experiments show that the proposed framework is effective for attribute reduction in interval-valued information systems.

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